Using Machine Learning Algorithms to Detect Anomalies in the Solar Heating System

Abstract

This article explores the use of machine learning algorithms to identify anomalies in the solar heating system. A solar heating system that has been developed consists of several parts to simplify the description and modeling process. The authors propose a new architecture for neural networks based on ordinary differential equations. The idea is to apply the new architecture for practical problems of accident prediction (the problem of extrapolation of time series) and classification (classification of accidents based on historical data). The developed machine learning algorithms, artificial intelligence techniques, the theory of differential equations - these directions allow us to build a model for predicting the system's accident rate. The theory of database management (non-relational databases) - these systems allow you to establish the optimal storage of large time series.